{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy,pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = arcpy.da.FeatureClassToNumPyArray(\"./data/weather.shp\",\n",
    "                                       [\"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 目标累计分布函数图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14e650b2da0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "sortMean = numpy.sort(dt[\"mean\"])\n",
    "#yvals是每一个样本的分位数\n",
    "yvals = numpy.arange(len(sortMean))/float(len(sortMean))\n",
    "plt.plot(sortMean, yvals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QQ散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14e65148d68>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "#对目标累计分布函数值求标准正太分布累计分布函数的逆\n",
    "x_label = st.norm.ppf(yvals)\n",
    "plt.scatter(x_label, sortMean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正态QQ图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "st.probplot(dt[\"mean\"], dist=\"norm\", plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
